In many instances it is desirable to locate a specific object from a collection. For example, a person may wish to locate a physical report in a library, or a specific document or image file on their hard disk, computer database or on the web. However in many instances, the person may only have an imperfect recollection of one or more characteristics of the desired object, or possibly the characteristics of a related object (or objects) and the nature of the relationship to the desired object. These characteristics may relate to the actual document (e.g. title, author, or text), a property of the document (size, shape, or cover image), or even related documents which are located nearby or were created at a similar time, day or place. In other cases a person may not have a specific object in mind, but may simply wish to browse a complete or large collection on the basis of a list of characteristics of interest, and then see which items appeal to them.
Some of these characteristics may easily be searched if known, such as title, author, creation date, publication date and reference number. However in many cases the remembered characteristics are not easily searchable or are only indirectly related to the desired object and are thus typically insufficient to allow identification of the desired document in the collection, particularly when using computational search tools. For example, they may relate the approximate physical location on a shelf in a library, the colour of nearby reports, the approximate relative location to distinctive nearby reports, the visual layout of the front page, the colour of the front page, a distinctive feature on the front page (e.g. a logo), or perhaps a feature in the image such as a specific person, or object (e.g. Eiffel tower) who were associated with the object sought. In some cases if one can identify the location of a related image (i.e. one having a shared or related characteristic) this location can be used to locate the desired object. For example if one can locate a image file with the Eiffel tower in it, one could locate the directory the image was located in, and this directory could be browsed to locate the desired object.
In the case where the objects are computer files, or are represented in a database, a computational search engine with a graphical user interface may be used to locate a desired object. This approach allows a user to enter one or more properties of the desired object, and the search engine will attempt to find and display all objects satisfying the search criteria. Typically the search engine will have a user interface which allows the results to be displayed in a range of formats such as a 2 dimensional (2D) grid of thumbnail images (visual representations), icons, or filenames, or as a detailed 1 dimensional (1D) list of filenames and file properties (in which each column is a property). Other alternatives have been explored, such as network graphs, hierarchical clusters, as well as 2.5 (eg distributed on an inclined plane) and 3D approaches in which 3 search characteristics or search terms define each of the 3 axes. Some user interfaces such as tree file explorers provided by the operating system allow the items to be sorted based on filenames or associated file properties (e.g. file type, creation date, file size etc.). Whilst this approach allows a user to view objects in a range of directories on a hard disk (or in a database or on the internet, etc), according to various criteria, this approach is only usable if the remembered characteristic is a searchable characteristic. Also in many cases multiple search terms may be used to describe a characteristic and if the user inputs a poor or narrow search term, then they may miss potentially relevant results.
If the characteristic is not searchable then the user can attempt to find the desired object by using a graphical file manager or similar application to browse through directories or locations which might contain the desired object. Such graphical file managers typically have a user interface similar to search engines in which results are presented in a 2D grid or list (often a search engine is integrated into a file manager application). However one limitation with this approach is that as the number of files in the directory increases it is difficult to see all, or at least a large percentage of the items, at the one time.
Further, even when a large collection of results are displayed in 2D or 3D, the representation of these results is often less than optimal. In some cases they are simply arranged on a regular grid losing all concept of the actual order and separation of the results. In other cases the results are displayed based upon the value of the searched characteristics (ie as a coordinate point). However the search results will frequently include a number of clusters or be clumped in a single large cluster containing most of the data which often occurs when there are a few points which are located at a large distance from other points, and such effects makes identification of individual objects in the collection difficult, thereby negating the effectiveness of the visualisation technique used.
There is thus a need to provide an improved method for displaying a collection of objects.